Several latent variable models have been proposed for counting observations. These include Latent Semantic Analysis [1]}, Multinomial PCA [2]}, and Latent Dirichlet Allocation (LDA) [3]}. LDA is the most closely related model to the model proposed here since
it is also a Bayesian graphical model for count data and uses multinomial distribution. The main difference is that LDA uses a Dirichlet distributed latent space instead of a Gaussian distributed latent space. Our Gaussian distributed latent space
makes it possible to recover a non-trivial covariance structure among the count variables, unlike LDA [4]}, [5]}.
